{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping Reddit\n",
    "\n",
    "[Reddit](http://reddit.com) is a discussion board that bills itself as the \"Front Page of the Internet\". It is divided into a large number of topic-specific \"subreddits\". In this demo, we'll take some data about which subreddits various active Reddit users post to a lot to make a visual map of subreddits. The data comes from the paper [Navigating the massive world of reddit](http://arxiv.org/abs/1312.3387)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializing `pymldb`\n",
    "\n",
    "In this demo, we will use `pymldb` to interact with the [REST API](../../../../doc/#builtin/WorkingWithRest.md.html): see the [Using `pymldb` Tutorial](../../../../doc/nblink.html#_tutorials/Using pymldb Tutorial) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymldb import Connection\n",
    "mldb = Connection(\"http://localhost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading up the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/import_reddit</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:43.374825Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;numLineErrors&quot;</span>: <span style=\"color: #0000dd\">0</span>\n",
       "      }, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:45.0417228Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:43.374753Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;quoteChar&quot;</span>: <span style=\"color: #0000dd\">&quot;&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;delimiter&quot;</span>: <span style=\"color: #0000dd\">&quot;&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;outputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_raw&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;dataFileUrl&quot;</span>: <span style=\"color: #0000dd\">&quot;http://public.mldb.ai/reddit.csv.gz&quot;</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;import.text&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;import_reddit&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;import.text&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;import_reddit&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/import_reddit', { \n",
    "    \"type\": \"import.text\",  \n",
    "    \"params\": { \n",
    "        \"dataFileUrl\": \"http://public.mldb.ai/reddit.csv.gz\",\n",
    "        'delimiter':'', \n",
    "        'quoteChar':'',\n",
    "        'outputDataset': 'reddit_raw',\n",
    "        'runOnCreation': True\n",
    "    } \n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is what our raw dataset looks like. The `lineText` column will need to be parsed: it's comma-delimited, with the first token being a user ID and the remaining tokens being the set of subreddits that user contributed to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineText</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>603,politics,trees,pics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>604,Metal,AskReddit,tattoos,redditguild,WTF,co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>605,politics,IAmA,AdviceAnimals,movies,smallbu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>606,CrohnsDisease,birthcontrol,IAmA,AdviceAnim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>607,space,Fitment,cars,Economics,Libertarian,2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   lineText\n",
       "_rowName                                                   \n",
       "1                                   603,politics,trees,pics\n",
       "2         604,Metal,AskReddit,tattoos,redditguild,WTF,co...\n",
       "3         605,politics,IAmA,AdviceAnimals,movies,smallbu...\n",
       "4         606,CrohnsDisease,birthcontrol,IAmA,AdviceAnim...\n",
       "5         607,space,Fitment,cars,Economics,Libertarian,2..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.query(\"select * from reddit_raw limit 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforming the raw data into a sparse matrix\n",
    "\n",
    "\n",
    "We will create and run a [Procedure](../../../../doc/#builtin/procedures/Procedures.md.html) of type [`transform`](../../../../doc/#builtin/procedures/TransformDataset.md.html). The `tokenize` function will project out the subreddit names into columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/reddit_import</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:45.3741944Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;columnCount&quot;</span>: <span style=\"color: #0000dd\">15122</span>, \n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;rowCount&quot;</span>: <span style=\"color: #0000dd\">876961</span>, \n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;valueCount&quot;</span>: <span style=\"color: #0000dd\">892067</span>\n",
       "      }, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:51.7200754Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:45.374114Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;outputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_dataset&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;inputData&quot;</span>: <span style=\"color: #0000dd\">&quot;select tokenize(lineText, {offset: 1, value: 1}) as * from reddit_raw&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;transform&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_import&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;transform&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_import&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/reddit_import', {\n",
    "    \"type\": \"transform\",\n",
    "    \"params\": {\n",
    "        \"inputData\": \"select tokenize(lineText, {offset: 1, value: 1}) as * from reddit_raw\",\n",
    "        \"outputDataset\": \"reddit_dataset\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the resulting dataset: it's a sparse matrix with a row per user and a column per subreddit, where the cells are `1` if the row's user was a contributor to the column's subreddit, and `null` otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AdviceAnimals</th>\n",
       "      <th>AskReddit</th>\n",
       "      <th>LucidDreaming</th>\n",
       "      <th>Mustang</th>\n",
       "      <th>WTF</th>\n",
       "      <th>cars</th>\n",
       "      <th>ems</th>\n",
       "      <th>funny</th>\n",
       "      <th>guns</th>\n",
       "      <th>motorcycles</th>\n",
       "      <th>...</th>\n",
       "      <th>fffffffuuuuuuuuuuuu</th>\n",
       "      <th>gaming</th>\n",
       "      <th>movies</th>\n",
       "      <th>\"reddit.com\"</th>\n",
       "      <th>trees</th>\n",
       "      <th>videos</th>\n",
       "      <th>breakingbad</th>\n",
       "      <th>cringe</th>\n",
       "      <th>darksouls</th>\n",
       "      <th>thatHappened</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>785489</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754094</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785459</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785298</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785140</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          AdviceAnimals  AskReddit  LucidDreaming  Mustang  WTF  cars  ems  \\\n",
       "_rowName                                                                     \n",
       "785489                1          1              1        1    1     1    1   \n",
       "754094              NaN          1            NaN      NaN  NaN   NaN  NaN   \n",
       "785459                1          1            NaN      NaN    1     1  NaN   \n",
       "785298              NaN          1            NaN      NaN  NaN   NaN  NaN   \n",
       "785140              NaN        NaN            NaN      NaN  NaN   NaN  NaN   \n",
       "\n",
       "          funny  guns  motorcycles      ...       fffffffuuuuuuuuuuuu  gaming  \\\n",
       "_rowName                                ...                                     \n",
       "785489        1     1            1      ...                       NaN     NaN   \n",
       "754094      NaN   NaN          NaN      ...                       NaN     NaN   \n",
       "785459        1   NaN          NaN      ...                         1       1   \n",
       "785298      NaN   NaN          NaN      ...                       NaN     NaN   \n",
       "785140      NaN   NaN          NaN      ...                       NaN     NaN   \n",
       "\n",
       "          movies  \"reddit.com\"  trees  videos  breakingbad  cringe  darksouls  \\\n",
       "_rowName                                                                        \n",
       "785489       NaN           NaN    NaN     NaN          NaN     NaN        NaN   \n",
       "754094       NaN           NaN    NaN     NaN          NaN     NaN        NaN   \n",
       "785459         1             1      1       1          NaN     NaN        NaN   \n",
       "785298       NaN           NaN    NaN     NaN          NaN     NaN        NaN   \n",
       "785140       NaN           NaN    NaN     NaN            1       1          1   \n",
       "\n",
       "          thatHappened  \n",
       "_rowName                \n",
       "785489             NaN  \n",
       "754094             NaN  \n",
       "785459             NaN  \n",
       "785298             NaN  \n",
       "785140               1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.query(\"select * from reddit_dataset limit 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction with Singular Value Decomposition (SVD)\n",
    "\n",
    "\n",
    "We will create and run a [Procedure](../../../../doc/#builtin/procedures/Procedures.md.html) of type [`svd.train`](../../../../doc/#builtin/procedures/Svd.md.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/reddit_svd</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:54.4468389Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:44:44.2194996Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:43:54.446762Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;columnOutputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_svd_embedding&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;trainingData&quot;</span>: <span style=\"color: #0000dd\">&quot;\\n            SELECT \\n                COLUMN EXPR (AS columnName() ORDER BY rowCount() DESC, columnName() LIMIT 4000) \\n            FROM reddit_dataset\\n        &quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;svd.train&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_svd&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;svd.train&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_svd&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/reddit_svd', {\n",
    "    \"type\" : \"svd.train\",\n",
    "    \"params\" : {\n",
    "        \"trainingData\" : \"\"\"\n",
    "            SELECT \n",
    "                COLUMN EXPR (AS columnName() ORDER BY rowCount() DESC, columnName() LIMIT 4000) \n",
    "            FROM reddit_dataset\n",
    "        \"\"\",\n",
    "        \"columnOutputDataset\" : \"reddit_svd_embedding\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of this operation is a new dataset with a row per subreddit for the 4000 most-active subreddits and columns representing coordinates for that subreddit in a 100-dimensional space. \n",
    "\n",
    "**Note:** the row names are the subreddit names followed by \".numberEquals.1\" because the SVD training procedure interpreted the input matrix as categorical rather than numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>embedding.0</th>\n",
       "      <th>embedding.10</th>\n",
       "      <th>embedding.11</th>\n",
       "      <th>embedding.12</th>\n",
       "      <th>embedding.13</th>\n",
       "      <th>embedding.14</th>\n",
       "      <th>embedding.15</th>\n",
       "      <th>embedding.16</th>\n",
       "      <th>embedding.17</th>\n",
       "      <th>embedding.18</th>\n",
       "      <th>...</th>\n",
       "      <th>embedding.91</th>\n",
       "      <th>embedding.92</th>\n",
       "      <th>embedding.93</th>\n",
       "      <th>embedding.94</th>\n",
       "      <th>embedding.95</th>\n",
       "      <th>embedding.96</th>\n",
       "      <th>embedding.97</th>\n",
       "      <th>embedding.98</th>\n",
       "      <th>embedding.99</th>\n",
       "      <th>embedding.9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AskReddit.numberEquals.1</th>\n",
       "      <td>-0.448854</td>\n",
       "      <td>0.002584</td>\n",
       "      <td>0.071048</td>\n",
       "      <td>0.004983</td>\n",
       "      <td>-0.004218</td>\n",
       "      <td>-0.000756</td>\n",
       "      <td>-0.014381</td>\n",
       "      <td>0.039270</td>\n",
       "      <td>0.011690</td>\n",
       "      <td>0.005458</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001824</td>\n",
       "      <td>-0.001005</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.002573</td>\n",
       "      <td>-0.002802</td>\n",
       "      <td>-0.004249</td>\n",
       "      <td>0.006107</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>0.066779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funny.numberEquals.1</th>\n",
       "      <td>-0.400773</td>\n",
       "      <td>-0.096315</td>\n",
       "      <td>-0.002975</td>\n",
       "      <td>0.027832</td>\n",
       "      <td>0.006565</td>\n",
       "      <td>-0.009726</td>\n",
       "      <td>0.020593</td>\n",
       "      <td>0.019796</td>\n",
       "      <td>-0.006885</td>\n",
       "      <td>-0.020714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>-0.000875</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>-0.001406</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>-0.002564</td>\n",
       "      <td>-0.002319</td>\n",
       "      <td>0.311920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pics.numberEquals.1</th>\n",
       "      <td>-0.380045</td>\n",
       "      <td>0.041943</td>\n",
       "      <td>-0.038168</td>\n",
       "      <td>-0.073641</td>\n",
       "      <td>0.054020</td>\n",
       "      <td>0.070904</td>\n",
       "      <td>0.024925</td>\n",
       "      <td>-0.028070</td>\n",
       "      <td>0.015659</td>\n",
       "      <td>0.013717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004381</td>\n",
       "      <td>-0.003049</td>\n",
       "      <td>-0.001057</td>\n",
       "      <td>-0.003187</td>\n",
       "      <td>-0.000768</td>\n",
       "      <td>-0.001563</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>-0.001195</td>\n",
       "      <td>-0.002240</td>\n",
       "      <td>-0.468059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WTF.numberEquals.1</th>\n",
       "      <td>-0.294057</td>\n",
       "      <td>0.131549</td>\n",
       "      <td>-0.170047</td>\n",
       "      <td>-0.081603</td>\n",
       "      <td>0.048413</td>\n",
       "      <td>-0.017429</td>\n",
       "      <td>-0.013951</td>\n",
       "      <td>-0.024105</td>\n",
       "      <td>-0.052710</td>\n",
       "      <td>0.028045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.003586</td>\n",
       "      <td>0.003579</td>\n",
       "      <td>-0.000195</td>\n",
       "      <td>0.004218</td>\n",
       "      <td>-0.001208</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.003942</td>\n",
       "      <td>0.003031</td>\n",
       "      <td>0.005529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaming.numberEquals.1</th>\n",
       "      <td>-0.263956</td>\n",
       "      <td>-0.123462</td>\n",
       "      <td>-0.109681</td>\n",
       "      <td>0.100280</td>\n",
       "      <td>0.050675</td>\n",
       "      <td>-0.071520</td>\n",
       "      <td>-0.129815</td>\n",
       "      <td>0.166366</td>\n",
       "      <td>0.046277</td>\n",
       "      <td>-0.155068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005391</td>\n",
       "      <td>-0.005024</td>\n",
       "      <td>0.011013</td>\n",
       "      <td>-0.003873</td>\n",
       "      <td>0.002043</td>\n",
       "      <td>0.009336</td>\n",
       "      <td>-0.001159</td>\n",
       "      <td>0.001016</td>\n",
       "      <td>0.002622</td>\n",
       "      <td>-0.062798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          embedding.0  embedding.10  embedding.11  \\\n",
       "_rowName                                                            \n",
       "AskReddit.numberEquals.1    -0.448854      0.002584      0.071048   \n",
       "funny.numberEquals.1        -0.400773     -0.096315     -0.002975   \n",
       "pics.numberEquals.1         -0.380045      0.041943     -0.038168   \n",
       "WTF.numberEquals.1          -0.294057      0.131549     -0.170047   \n",
       "gaming.numberEquals.1       -0.263956     -0.123462     -0.109681   \n",
       "\n",
       "                          embedding.12  embedding.13  embedding.14  \\\n",
       "_rowName                                                             \n",
       "AskReddit.numberEquals.1      0.004983     -0.004218     -0.000756   \n",
       "funny.numberEquals.1          0.027832      0.006565     -0.009726   \n",
       "pics.numberEquals.1          -0.073641      0.054020      0.070904   \n",
       "WTF.numberEquals.1           -0.081603      0.048413     -0.017429   \n",
       "gaming.numberEquals.1         0.100280      0.050675     -0.071520   \n",
       "\n",
       "                          embedding.15  embedding.16  embedding.17  \\\n",
       "_rowName                                                             \n",
       "AskReddit.numberEquals.1     -0.014381      0.039270      0.011690   \n",
       "funny.numberEquals.1          0.020593      0.019796     -0.006885   \n",
       "pics.numberEquals.1           0.024925     -0.028070      0.015659   \n",
       "WTF.numberEquals.1           -0.013951     -0.024105     -0.052710   \n",
       "gaming.numberEquals.1        -0.129815      0.166366      0.046277   \n",
       "\n",
       "                          embedding.18     ...       embedding.91  \\\n",
       "_rowName                                   ...                      \n",
       "AskReddit.numberEquals.1      0.005458     ...          -0.001824   \n",
       "funny.numberEquals.1         -0.020714     ...           0.000278   \n",
       "pics.numberEquals.1           0.013717     ...           0.004381   \n",
       "WTF.numberEquals.1            0.028045     ...           0.000223   \n",
       "gaming.numberEquals.1        -0.155068     ...           0.005391   \n",
       "\n",
       "                          embedding.92  embedding.93  embedding.94  \\\n",
       "_rowName                                                             \n",
       "AskReddit.numberEquals.1     -0.001005      0.002710      0.001701   \n",
       "funny.numberEquals.1          0.000375      0.001001     -0.000875   \n",
       "pics.numberEquals.1          -0.003049     -0.001057     -0.003187   \n",
       "WTF.numberEquals.1            0.003586      0.003579     -0.000195   \n",
       "gaming.numberEquals.1        -0.005024      0.011013     -0.003873   \n",
       "\n",
       "                          embedding.95  embedding.96  embedding.97  \\\n",
       "_rowName                                                             \n",
       "AskReddit.numberEquals.1      0.002573     -0.002802     -0.004249   \n",
       "funny.numberEquals.1          0.000552     -0.001406      0.001127   \n",
       "pics.numberEquals.1          -0.000768     -0.001563      0.000897   \n",
       "WTF.numberEquals.1            0.004218     -0.001208      0.001949   \n",
       "gaming.numberEquals.1         0.002043      0.009336     -0.001159   \n",
       "\n",
       "                          embedding.98  embedding.99  embedding.9  \n",
       "_rowName                                                           \n",
       "AskReddit.numberEquals.1      0.006107     -0.000439     0.066779  \n",
       "funny.numberEquals.1         -0.002564     -0.002319     0.311920  \n",
       "pics.numberEquals.1          -0.001195     -0.002240    -0.468059  \n",
       "WTF.numberEquals.1            0.003942      0.003031     0.005529  \n",
       "gaming.numberEquals.1         0.001016      0.002622    -0.062798  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.query(\"select * from reddit_svd_embedding limit 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering with K-Means\n",
    "\n",
    "\n",
    "We will create and run a [Procedure](../../../../doc/#builtin/procedures/Procedures.md.html) of type [`kmeans.train`](../../../../doc/#builtin/procedures/KmeansProcedure.md.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/reddit_kmeans</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:44:58.9105241Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:44:59.093301Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:44:58.910438Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;numClusters&quot;</span>: <span style=\"color: #0000dd\">20</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;outputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_kmeans_clusters&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;trainingData&quot;</span>: <span style=\"color: #0000dd\">&quot;select * from reddit_svd_embedding&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;kmeans.train&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_kmeans&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;kmeans.train&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_kmeans&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/reddit_kmeans', {\n",
    "    \"type\" : \"kmeans.train\",\n",
    "    \"params\" : {\n",
    "        \"trainingData\" : \"select * from reddit_svd_embedding\",\n",
    "        \"outputDataset\" : \"reddit_kmeans_clusters\",\n",
    "        \"numClusters\" : 20,\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of this operation is a simple dataset which associates each row in the input (i.e. each subreddit) to one of 20 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usyd.numberEquals.1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mommit.numberEquals.1</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minnesotatwins.numberEquals.1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HongKong.numberEquals.1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>niggers.numberEquals.1</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               cluster\n",
       "_rowName                              \n",
       "usyd.numberEquals.1                  5\n",
       "Mommit.numberEquals.1               16\n",
       "minnesotatwins.numberEquals.1        3\n",
       "HongKong.numberEquals.1              2\n",
       "niggers.numberEquals.1              18"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.query(\"select * from reddit_kmeans_clusters limit 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-d Dimensionality Reduction with t-SNE\n",
    "\n",
    "\n",
    "We will create and run a [Procedure](../../../../doc/#builtin/procedures/Procedures.md.html) of type [`tsne.train`](../../../../doc/#builtin/procedures/TsneProcedure.md.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/reddit_tsne</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:45:01.2912471Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:45:12.5964549Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:45:01.291181Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;trainingData&quot;</span>: <span style=\"color: #0000dd\">&quot;select * from reddit_svd_embedding&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;rowOutputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_tsne_embedding&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;tsne.train&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_tsne&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;tsne.train&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_tsne&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/reddit_tsne', {\n",
    "    \"type\" : \"tsne.train\",\n",
    "    \"params\" : {\n",
    "        \"trainingData\" : \"select * from reddit_svd_embedding\",\n",
    "        \"rowOutputDataset\" : \"reddit_tsne_embedding\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is similar to the SVD step above: we get a row per subreddit and the columns are coordinates, but this time in a 2-dimensional space appropriate for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usyd.numberEquals.1</th>\n",
       "      <td>-30.012499</td>\n",
       "      <td>4.485752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mommit.numberEquals.1</th>\n",
       "      <td>8.447520</td>\n",
       "      <td>-47.081745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minnesotatwins.numberEquals.1</th>\n",
       "      <td>-50.094040</td>\n",
       "      <td>-18.705278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HongKong.numberEquals.1</th>\n",
       "      <td>-34.990097</td>\n",
       "      <td>1.323213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>niggers.numberEquals.1</th>\n",
       "      <td>26.637928</td>\n",
       "      <td>-0.875668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       x          y\n",
       "_rowName                                           \n",
       "usyd.numberEquals.1           -30.012499   4.485752\n",
       "Mommit.numberEquals.1           8.447520 -47.081745\n",
       "minnesotatwins.numberEquals.1 -50.094040 -18.705278\n",
       "HongKong.numberEquals.1       -34.990097   1.323213\n",
       "niggers.numberEquals.1         26.637928  -0.875668"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.query(\"select * from reddit_tsne_embedding limit 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting the number of users per subreddit\n",
    "\n",
    "\n",
    "We will create and run a [Procedure](../../../../doc/#builtin/procedures/Procedures.md.html) of type [`transform`](../../../../doc/#builtin/procedures/TransformDataset.md.html) on the transpose of the original input dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/reddit_count_users</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:45:20.0345097Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;columnCount&quot;</span>: <span style=\"color: #0000dd\">1</span>, \n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;rowCount&quot;</span>: <span style=\"color: #0000dd\">15122</span>, \n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;valueCount&quot;</span>: <span style=\"color: #0000dd\">15123</span>\n",
       "      }, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:45:21.165891Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-05-28T03:45:20.034423Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;outputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_user_counts&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;inputData&quot;</span>: <span style=\"color: #0000dd\">&quot;select columnCount() as numUsers from transpose(reddit_dataset)&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;transform&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_count_users&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;transform&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;reddit_count_users&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/reddit_count_users', {\n",
    "    \"type\": \"transform\",\n",
    "    \"params\": {\n",
    "        \"inputData\": \"select columnCount() as numUsers from transpose(reddit_dataset)\",\n",
    "        \"outputDataset\": \"reddit_user_counts\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We appended \"|1\" to the row names in this dataset to allow the `merge` operation below to work well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numUsers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>oracle</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>notcollectingstamps</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>notarealsub</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norge</th>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>police</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     numUsers\n",
       "_rowName                     \n",
       "oracle                     27\n",
       "notcollectingstamps         1\n",
       "notarealsub                 1\n",
       "norge                     446\n",
       "police                     89"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.query(\"select * from reddit_user_counts limit 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying and Visualizating the output\n",
    "\n",
    "We'll use the [Query API](../../../../doc/#builtin/sql/QueryAPI.md.html) to get the data into a Pandas DataFrame and then use Bokeh to visualize it.\n",
    "\n",
    "In the query below we renamed the rows to get rid of the \"|1\" which the SVD appended to each subreddit name and we filter out rows where `cluster` is `null` because we only clustered the 4000 most-active subreddits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>grid_x</th>\n",
       "      <th>grid_y</th>\n",
       "      <th>numUsers</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AskReddit</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-42</td>\n",
       "      <td>523005</td>\n",
       "      <td>-1.118738</td>\n",
       "      <td>-43.699772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funny</th>\n",
       "      <td>3</td>\n",
       "      <td>-49</td>\n",
       "      <td>0</td>\n",
       "      <td>396478</td>\n",
       "      <td>-45.672779</td>\n",
       "      <td>-1.286311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pics</th>\n",
       "      <td>0</td>\n",
       "      <td>-35</td>\n",
       "      <td>-42</td>\n",
       "      <td>362588</td>\n",
       "      <td>-33.671326</td>\n",
       "      <td>-43.146740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WTF</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>-49</td>\n",
       "      <td>262293</td>\n",
       "      <td>21.992300</td>\n",
       "      <td>-49.596294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaming</th>\n",
       "      <td>13</td>\n",
       "      <td>-14</td>\n",
       "      <td>49</td>\n",
       "      <td>255763</td>\n",
       "      <td>-12.789426</td>\n",
       "      <td>48.143551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cluster  grid_x  grid_y  numUsers          x          y\n",
       "_rowName                                                          \n",
       "AskReddit        2       0     -42    523005  -1.118738 -43.699772\n",
       "funny            3     -49       0    396478 -45.672779  -1.286311\n",
       "pics             0     -35     -42    362588 -33.671326 -43.146740\n",
       "WTF             16      21     -49    262293  21.992300 -49.596294\n",
       "gaming          13     -14      49    255763 -12.789426  48.143551"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = mldb.query(\"\"\"\n",
    "    select c.* as *, m.* as *, quantize(m.x, 7) as grid_x, quantize(m.y, 7) as grid_y \n",
    "    named c.rowName() \n",
    "    from merge(reddit_tsne_embedding, reddit_kmeans_clusters) as m\n",
    "        join reddit_user_counts as c on c.rowName() = m.rowPathElement(0)\n",
    "    where m.cluster is not null \n",
    "    order by c.numUsers desc\n",
    "\"\"\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "colormap = np.array([\n",
    "    \"#1f77b4\", \"#aec7e8\", \"#ff7f0e\", \"#ffbb78\", \"#2ca02c\", \n",
    "    \"#98df8a\", \"#d62728\", \"#ff9896\", \"#9467bd\", \"#c5b0d5\", \n",
    "    \"#8c564b\", \"#c49c94\", \"#e377c2\", \"#f7b6d2\", \"#7f7f7f\", \n",
    "    \"#c7c7c7\", \"#bcbd22\", \"#dbdb8d\", \"#17becf\", \"#9edae5\"\n",
    "])\n",
    "\n",
    "import bokeh.plotting as bp\n",
    "from bokeh.models import HoverTool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-banner\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"aeb4a7f1-07d1-4882-a4ed-840dc870b07b\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      Bokeh.$(\"#aeb4a7f1-07d1-4882-a4ed-840dc870b07b\").text(\"BokehJS successfully loaded\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i](window.Bokeh);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this line must be in its own cell \n",
    "bp.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"e6df556f-1bf6-413d-b9b2-b509a1469f6c\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"e6df556f-1bf6-413d-b9b2-b509a1469f6c\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'e6df556f-1bf6-413d-b9b2-b509a1469f6c' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
       "            var docs_json = 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Map by t-SNE\",\"tool_events\":{\"id\":\"e4831753-a50a-4e1b-b3e0-74ce5997602c\",\"type\":\"ToolEvents\"},\"toolbar_location\":null,\"tools\":[{\"id\":\"02a72713-e00d-433f-95d8-d440ea879963\",\"type\":\"HoverTool\"}],\"x_range\":{\"id\":\"a9f6e240-b343-4fed-af41-43de97f25dd1\",\"type\":\"DataRange1d\"},\"y_range\":{\"id\":\"55d7c06d-6240-4383-a3ff-973a8af730fd\",\"type\":\"DataRange1d\"}},\"id\":\"bfcbd645-2289-454e-8039-eff814e5ff51\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.6},\"fill_color\":{\"field\":\"fill_color\"},\"line_alpha\":{\"value\":0.6},\"line_color\":{\"field\":\"line_color\"},\"radius\":{\"field\":\"radius\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2c8b0b81-836a-413a-8dcf-3138584fffdd\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null},\"id\":\"55d7c06d-6240-4383-a3ff-973a8af730fd\",\"type\":\"DataRange1d\"},{\"attributes\":{\"data_source\":{\"id\":\"f3806a87-8ae6-4bee-b228-c722cbc1266a\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"82a1af77-1bb3-4492-99d9-e4ee97630b4b\",\"type\":\"Text\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"03677ea5-bf3d-4546-bf3a-61667831d94a\",\"type\":\"Text\"},\"selection_glyph\":null},\"id\":\"4a043674-34e0-4b36-974a-da32d08de06c\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"bfcbd645-2289-454e-8039-eff814e5ff51\"]},\"title\":\"Bokeh Application\",\"version\":\"0.11.1\"}};\n",
       "            var render_items = [{\"docid\":\"768274b5-6a84-486e-98a5-b33bfa882d01\",\"elementid\":\"e6df556f-1bf6-413d-b9b2-b509a1469f6c\",\"modelid\":\"bfcbd645-2289-454e-8039-eff814e5ff51\",\"notebook_comms_target\":\"259bf833-4f0f-419a-ba7e-1d189b4cf08f\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[20]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7f2808450bd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = bp.figure(plot_width=900, plot_height=700, title=\"Subreddit Map by t-SNE\",\n",
    "       tools=[HoverTool( tooltips=[ (\"/r/\", \"@subreddit\") ] )], toolbar_location=None,\n",
    "       x_axis_type=None, y_axis_type=None, min_border=1)\n",
    "x.scatter(\n",
    "    x = df.x.values, \n",
    "    y=df.y.values, \n",
    "    color=colormap[df.cluster.astype(int).values],\n",
    "    alpha=0.6,\n",
    "    radius=(df.numUsers.values ** .3)/15,\n",
    "    source=bp.ColumnDataSource({\"subreddit\": df.index.values})\n",
    ")\n",
    "\n",
    "labels = df.reset_index().groupby(['grid_x', 'grid_y'], as_index=False).first()\n",
    "labels = labels[labels[\"numUsers\"] > 10000]\n",
    "x.text(\n",
    "    x = labels.x.values, \n",
    "    y = labels.y.values,\n",
    "    text = labels._rowName.values,\n",
    "    text_align=\"center\", text_baseline=\"middle\",\n",
    "    text_font_size=\"8pt\", text_font_style=\"bold\",\n",
    "    text_color=\"#333333\"\n",
    ")\n",
    "\n",
    "bp.show(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Where to next?\n",
    "\n",
    "Check out the other [Tutorials and Demos](../../../../doc/#builtin/Demos.md.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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